INFORMATION SCIENCE FOR NETWORKS
Comprehending networks and networked behavior has become one of the leading intellectual obstacles of the 21st century. While we certainly grasp the innovation to designer transformational networks– from interaction infrastructure to on the internet social media networks– our academic understanding of basic phenomena that arise in networked systems remains limited. My goal is to combine network scientific research and also signal handling in order to leverage the structure of networks to better recognize data defined on them. In this context, the term Data Science for Networks can be recognized as a joint effort to understand both network frameworks and also network information. I will certainly introduce the essential foundation of graph signal handling (GSP) as a toolbox to study network data, and display its broad applicability by delving deeper into exactly how networks can aid us to understand Shakespearean authorship.
Dr. Santiago Segarra signed up with the Department of Electrical and Computer Engineering at Rice University as an Assistant Professor in 2018. He completed his PhD at The University of Pennsylvania in 2016 in the Department of Electrical and also Systems Engineering with Prof. Alejandro Ribeiro as his expert. After that, he spent almost two years as a Postdoctoral Research Associate at the Institute for Data, Systems, and also Society at MIT. Before relocating to the U.S. for his graduate researches, he resided in Argentina, where he obtained his Industrial Engineering Bachelor Degree from the Instituto Tecnológico de Buenos Aires (ITBA) in 2011. Santiago’s research passions consist of network theory, information scientific research, machine learning, and chart signal handling. His research study focuses in networks, common information frameworks that associate with many areas, consisting of engineering, economics, sociology, and also medication.